Cover Image

FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yang, Zhiqin, Zhang, Yonggang, Li, Chenxin, Cheung, Yiu-ming, Han, Bo, Yuan, Yixuan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Federated Learning with Extremely Noisy Clients via Negative Distillation
by: Lu, Yang, et al.
Published: (2023)

Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering
by: Zhang, Yiqun, et al.
Published: (2026)

FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients
by: Wen, Tian, et al.
Published: (2026)

CADM: Cluster-customized Adaptive Distance Metric for Categorical Data Clustering
by: Chen, Taixi, et al.
Published: (2025)

FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion
by: Tang, Zhenheng, et al.
Published: (2024)

Classifying Long-tailed and Label-noise Data via Disentangling and Unlearning
by: Shu, Chen, et al.
Published: (2025)

Learning Order Forest for Qualitative-Attribute Data Clustering
by: Zhao, Mingjie, et al.
Published: (2026)

Robust Training of Federated Models with Extremely Label Deficiency
by: Zhang, Yonggang, et al.
Published: (2024)

Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models
by: Yang, Zihua, et al.
Published: (2026)

Categorical Data Clustering via Value Order Estimated Distance Metric Learning
by: Zhang, Yiqun, et al.
Published: (2024)

Asynchronous Federated Clustering with Unknown Number of Clusters
by: Zhang, Yunfan, et al.
Published: (2024)

HyReaL: Clustering Attributed Graph via Hyper-Complex Space Representation Learning
by: Chen, Junyang, et al.
Published: (2024)

FedSDR: Federated Self-Distillation with Rectification
by: Ren, Ziheng, et al.
Published: (2026)

Break the Tie: Learning Cluster-Customized Category Relationships for Categorical Data Clustering
by: Zhao, Mingjie, et al.
Published: (2025)

CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detection
by: Zhong, Zhijie, et al.
Published: (2025)

FedEL: Federated Elastic Learning for Heterogeneous Devices
by: Zhang, Letian, et al.
Published: (2025)

Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
by: Dai, Rong, et al.
Published: (2024)

MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence
by: Tian, Hongduan, et al.
Published: (2024)

FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)

FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
by: Jiang, Chutian, et al.
Published: (2024)

GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning
by: Huang, Jintao, et al.
Published: (2024)

FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation
by: Li, Xingyu, et al.
Published: (2021)

GuardFed: A Trustworthy Federated Learning Framework Against Dual-Facet Attacks
by: Li, Yanli, et al.
Published: (2025)

FedCova: Robust Federated Covariance Learning Against Noisy Labels
by: Zhong, Xiangyu, et al.
Published: (2026)

FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning
by: Yao, Yuan, et al.
Published: (2025)

FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection
by: Geng, Jiaxiang, et al.
Published: (2024)

FedDPC : Handling Data Heterogeneity and Partial Client Participation in Federated Learning
by: Sen, Mrinmay, et al.
Published: (2025)

FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients
by: Shen, Leming, et al.
Published: (2025)

FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices
by: Wang, Kaile, et al.
Published: (2026)

pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning
by: Yi, Liping, et al.
Published: (2024)

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity
by: Muhebwa, Aggrey, et al.
Published: (2025)

Conditional Equivalence of DPO and RLHF: Implicit Assumption, Failure Modes, and Provable Alignment
by: Yang, Zhiqin, et al.
Published: (2026)

FedIN: Federated Intermediate Layers Learning for Model Heterogeneity
by: Chan, Yun-Hin, et al.
Published: (2023)

Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data
by: Chen, Yiyue, et al.
Published: (2025)

Improve Knowledge Distillation via Label Revision and Data Selection
by: Lan, Weichao, et al.
Published: (2024)

Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering
by: Zhang, Yiqun, et al.
Published: (2024)

FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergence
by: Xie, Zhijie, et al.
Published: (2022)

FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
by: Ji, Xinyuan, et al.
Published: (2024)

pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
by: Yi, Liping, et al.
Published: (2024)

FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
by: Zhang, Yuxin, et al.
Published: (2024)